{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import dateutil\n",
    "import analysis\n",
    "\n",
    "packets = analysis.parse_packet_file('data/all.txt')\n",
    "packets += analysis.parse_packet_file('data/temp_basals.txt')\n",
    "\n",
    "# Select valid packets\n",
    "packets = filter(lambda x: x.is_valid(), packets)\n",
    "\n",
    "# Create a pandas dataframe\n",
    "df = analysis.packets_to_pandas(map(lambda x: x.as_dict(), packets))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.0     4036\n",
       "3.0      3004\n",
       "7.0      1140\n",
       "32.0     1005\n",
       "31.0      113\n",
       "5.0        76\n",
       "12.0       23\n",
       "50.0       11\n",
       "6.0        10\n",
       "21.0       10\n",
       "36.0        6\n",
       "24.0        5\n",
       "29.0        5\n",
       "25.0        5\n",
       "23.0        5\n",
       "205.0       3\n",
       "126.0       2\n",
       "0.0         2\n",
       "157.0       1\n",
       "Name: body_len, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Distribution of body lengths\n",
    "df.body_len.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Select packets with body length of 10\n",
    "fixed_length = df.loc[df[\"body_len\"] == 10]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Generate a correlation matrix of the message bits\n",
    "bits = map(analysis.hex_unpackbits, list(fixed_length[\"raw_packet\"].values))\n",
    "bits_df = pd.DataFrame(bits)\n",
    "corr = bits_df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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xaZpn81bHWBbr0ryyItbOes5qI7pJ\nqZ6vbGPE6jmflGxzsaEbTr75Cb+V5o+Nr8/9xIiIesPJadbokPqsjhExmRqt5pxmjQ6pz4jJ1Wi2\nGfOC9uZ07GHdk9L827FyIufC/HZkXJHmZxYbOh8eV6X5+mLj5symWDzrsTtLtYnkkI2Oq3vL0tiQ\n5rfEQ9O82iy02ox2iGoT1dNOzzd0nymul5tiv152Qxww6FyqDW0r2Qag9San+Ua81Sbi1cbV+8aN\naZ5tAFw1HNhVDK2hlXF9mlefFzNVTVQ1VF0v1ebq1ca1QzYonqbqup2U6nnJ6mtnPP5Kdd7VOVaf\n0ar6/05yr6vu/9U1tzy+m+bV+8LeyftINWd1n6t+n63GD6npqs6rDaorvtkDAAAAMCIWewAAAABG\nxGIPAAAAwIhY7AEAAAAYEYs9AAAAACOiG9c8k+2wXe10Xu3SXe1SvrjYYb7a1Tubd2GxG/9QQ+ac\ntur5mi+Gvv5Vl5bn/dx/yyf46zmd1i5p6HU7iRodOuc0a3RXr8+q69bV7b+n+d5dv7sO41PV+dDO\nSFntVse+P6u6ncyXx191wKzuf1WXmqoDUtZ1amfZEEt72e5F161K1b1o6L07e76qDlC7uml2wNw2\n8FfD6vqvzKfP//Pdhth7Z5/CXYa+PtX7X9V1csi9vrrmqmsr67oVkX/mru7bVc1Vr1E1vnp/GdIZ\nsuqMWPHNHgAAAIARsdgDAAAAMCIWewAAAABGxGIPAAAAwIhY7AEAAAAYEd245plXf/nDveyPH/+C\ndGy1M3rVvWBrscN4Nmc174f/66vTsXFNHr9rVZ6/+gvFnE/IH+s0Vc/X8Vd/oJe9/bDjp3ou2Y7x\nt8Ue6dglsTnNq/En/PU5aX7+LM+NYbUSMZkaHTrnNGt0vtdnxORqNOtI8u1YmY6tum7d2i7MD969\nb87nxc5z7rqT0vzofT6T5lWXjuoevTRu7WVVh44D4jtpPp+siLVpXj2m78byXra4eJ/7jW1/mOZ7\nXp3fL570+LwbZdbV5dj4SDr2sjgqzQ+Pq9L82jg4zW+IA9L85C+8o5e99vn5veJD+VM7uDNS1nmr\n7nSTf4acVL+s98Wretle8YN07HkTmnO+G1pDL4yL03yPLbPv3nP5oqek+T6xLs2vjsPS/KbYN82r\nLkVZXnVXmqaqPifl1qQDXkTELcn9b2nRRWpn2Bh7pXl1v6jOve5e2b+TVB29bi/eQ6tOWpUhHcaq\n8667EebnUs2ZdWmsuutWHb0qvtkDAAAAMCIWewAAAABGxGIPAAAAwIhY7AEAAAAYEYs9AAAAACPS\nuq6b7gStddOeAwAAAGBX0lqLruta9jPf7AEAAAAYEYs9AAAAACNisQcAAABgRCz2AAAAAIyIxR4A\nAACAEbHYAwAAADAiFnsAAAAARsRiDwAAAMCIWOwBAAAAGJHtLva01i5sra1rrX3jbtmDW2t/2Vq7\nvrV2WWttz+meJgAAAACzMZtv9lwUEUfdI/vtiLi867qVEfHfI+LkSZ8YAAAAAMNtd7Gn67orI+IH\n94ifFREfuPPPH4iIX5nweQEAAAAwB3Pds2dZ13XrIiK6rvteRCyb3CkBAAAAMFeT2qC5m9BxAAAA\nANgBu83x761rre3Tdd261tpPRMT6exu8evXqu/48MzMTMzMzc5wWAAAAYNezZs2aWLNmzazGtq7b\n/pdyWmv7RsQlXdcdfOd/nxsR3++67tzW2kkR8eCu6367+LvdbOYAAAAAYHZaa9F1XUt/tr2FmNba\nxRExExFLI2JdRKyKiE9FxEcjYkVE/H1E/Ieu6zYWf99iDwAAAMAE7dBizwQmt9gDAAAAMEH3ttgz\nqQ2aAQAAAJgHLPYAAAAAjIjFHgAAAIARsdgDAAAAMCIWewAAAABGxGIPAAAAwIhY7AEAAAAYEYs9\nAAAAACNisQcAAABgRCz2AAAAAIyIxR4AAACAEbHYAwAAADAiFnsAAAAARsRiDwAAAMCIWOwBAAAA\nGBGLPQAAAAAjYrEHAAAAYEQs9gAAAACMiMUeAAAAgBGx2AMAAAAwIhZ7AAAAAEbEYg8AAADAiFjs\nAQAAABgRiz0AAAAAI2KxBwAAAGBELPYAAAAAjIjFHgAAAIARsdgDAAAAMCIWewAAAABGxGIPAAAA\nwIhY7AEAAAAYEYs9AAAAACNisQcAAABgRCz2AAAAAIyIxR4AAACAEbHYAwAAADAiFnsAAAAARsRi\nDwAAAMCIbHexp7V2YWttXWvtG3fLntta+2ZrbVtr7Wene4oAAAAAzNZsvtlzUUQcdY/s2oh4dkT8\nj4mfEQAAAABzttv2BnRdd2Vr7ZH3yK6PiGittWmdGAAAAADD2bMHAAAAYES2+82eSVi9evVdf56Z\nmYmZmZn7YloAAACAUVizZk2sWbNmVmNb13XbH/Sjf8Z1Sdd1j75HfkVEvKHruq/dy9/tZjMHAAAA\nALPTWouu69LtdWb7z7janf+rfgYAAADAPLDdb/a01i6OiJmIWBoR6yJiVUT8ICLeFRF7R8TGiLim\n67pfKv6+b/YAAAAATNC9fbNnVv+Mawcnt9gDAAAAMEGT+GdcAAAAANwPWOwBAAAAGBGLPQAAAAAj\nYrEHAAAAYEQs9gAAAACMiMUeAAAAgBGx2AMAAAAwIhZ7AAAAAEbEYg8AAADAiFjsAQAAABgRiz0A\nAAAAI2KxBwAAAGBELPYAAAAAjIjFHgAAAIARsdgDAAAAMCIWewAAAABGxGIPAAAAwIhY7AEAAAAY\nEYs9AAAAACNisQcAAABgRCz2AAAAAIyIxR4AAACAEbHYAwAAADAiFnsAAAAARsRiDwAAAMCIWOwB\nAAAAGBGLPQAAAAAjYrEHAAAAYEQs9gAAAACMiMUeAAAAgBGx2AMAAAAwIhZ7AAAAAEbEYg8AAADA\niFjsAQAAABgRiz0AAAAAI2KxBwAAAGBELPYAAAAAjMh2F3taaxe21ta11r5xt+ytrbVvtdauaa19\nvLX2oOmeJgAAAACzMZtv9lwUEUfdI/vLiPjprusOiYj/FREnT/rEAAAAABhuu4s9XdddGRE/uEd2\nedd1P7zzP78UEQ+fwrkBAAAAMNAk9ux5RUT8+QSOAwAAAMAO2qHFntba70TEP3ddd/GEzgcAAACA\nHbDbXP9ia+1lEXF0RDxpe2NXr159159nZmZiZmZmrtMCAAAA7HLWrFkTa9asmdXY1nXd9ge1tm9E\nXNJ13cF3/vfTI+JtEfHErus2bOfvdrOZAwAAAIDZaa1F13Ut+9l2v9nTWrs4ImYiYmlr7eaIWBUR\np0TEwoj4q9ZaRMSXuq779Ymd8S6s/Uk/+4UX/VU69qi4LM0v6zVP+5GT4i1p/sw/+WyaZ/N+/pNP\nS8fGHXkcz8sX+tp78uG/cFx/zoWxJR372XhGMekwR0T+/F758af2skue8+R07DMifw6HOiNO6mVb\nYlE6dkHxpG8rynprLEzz8+LUWZ4dWX1GTLdGh9RnxHRrNKvPiOnW6JD6jJhcjZ4Spw8anzl3Xb+e\nIyK27fPAHT42973VLf0cFR/vvpLmF8Rr0vw/xnvT/KPxvF52faxMx94Q+6f5m+Jdab4zvDbemubV\ne9ResTEZuyAduykWp/mj49o0/1w8Mc0PiBt62bJYl469Jh6b5vsnx4iI2BxL0vy22CPNPxnPHGUa\nGQAAIABJREFU7mV/FU9Jx/5kfDfN/yJm0vzpsSbNPxFH97Lq8ewRt6X58v/7vTRflL8txI0P+Yk0\n/8mZ5DgPyY8Rn9g1/p/IQ2voujgozavPf5kFsa2YM6/FfeOmNF8Ra9N8QyxN86Vxr98d+FfOjVWz\nHjvUW+O1aT6pe+vZcWKaZ8/7+liWjj1/JzTBPi1OSfPdiutlU3H/q66j7P6/pbhuby/uobsX96jq\nHDfGXrM+RqWqxep3tK3F73RZjVY1UdVoZbuLPV3XvTCJLxo0CwAAAAD3iUl04wIAAABgnrDYAwAA\nADAiFnsAAAAARsRiDwAAAMCIbHeDZu5b3aH9bh9/F3nngqozxAsjbxn0mP/7jVnPWc3bHpV3QNjz\ngLwbQ39v9TvnfObs59wjbi+OMmzH9MoH4iVp/pNH9B/T4n/Md0bfvOdETiVuTboUrIxvp2NvieVp\nvlf8IM0fG9cUs+rGNVtDaiViMjU6dM5p1mg15zRrdEh9Rky3Ro+MK9K86i5x9D6fKY701R0/Ge5z\nVdet57THpfkRRQfMj3f5Pfd34uxZn0vVdedNsz7C9H226CRVdWnJuvosiU3p2KPj0jT/Qhye5lUn\nqawDzIbYe9bnd2/jqznPO/i0NF957fW97KlxeTr2O2lad3p8ejH+quT5urx43SpbH1h0eiqaDi6M\nrWn+nTV5h7lM3udyfIbW0Nvj9WlefV7MOgbtE+vTsVUt3hj7pvnGeHCa71N0u8seU/XeOk1PjM9P\n9fhVt7sHJ/eiL8fjp3ouQ9RdpHJL49ZBx1mXdB6rPlveMfC6yLpuRUQ8Lr7cy6rP7UO7y1XnWHXS\nWhyb0zxTdaOs+GYPAAAAwIhY7AEAAAAYEYs9AAAAACNisQcAAABgRCz2AAAAAIyIblzzTGv9TjoH\nxtfSsVVniJuKnfG/9sCfnfWc1byb9807A21Y9JA0j8g747Qu33X8wLiul90We6Rj833Rh3tG5B1z\nvrX54F62fln1OCfTGWx5fLeXXROHpGMfGrek+XeLrguXxDFp/tRZnhvDaiViMjU6dM5p1mhWnxHT\nrdEh9RkxuRpdFFt62Zlxejp2YTI2ou7SU/XFY367IF6T5lXXrVXx22n++TgnzbMuMFXnohPiHWke\nSXeRneXD8eI0r7pUZffFquvQ5fHkND+s6HRXdfW5Plb2sqrrUNVFpXo81X2x3ZTf018Zf9DLPhO/\nnI6N4r1lWdFJqbI0uf8vLz5b3BAHzPoYEXX3mqqT3Gvj93vZtvLXlGcV+bgMraE3xlvTPPtsGZHX\nRdYVKSJic1GL1etcXRdbI+/elt3rqpqbplfHe9P82gkd/yNxbJpn3cseP4/u59V9sbI2VqT5ouI9\nLbuOqo5u1fVcdamq7ovZa1F1Lq5+F6uu8+razTrgVaouYkPrwjd7AAAAAEbEYg8AAADAiFjsAQAA\nABgRiz0AAAAAI2KxBwAAAGBEdOOaZ764sr/b95ZYlI69NI5O85Pi3DR/RfzxrOes5v25B34lHXto\nXJ3m70/TiK887OfSPOu8Ue3cH0nnkrm4II5P8wWP7O+Yfk6cko49fyJnErEhlvayahf53Yrd2Ksu\nXUeUz5d+XLM1pFYiJlOjQ+ecZo1WnXGmWaND6jNicjW6LvbpZYfHVYOOUXXjifj5gWfDfPAfiy4t\nH+9OTfOq69aeLb9G3xB/maT96zAi4iF39N8rIiI25E2ndorq/ld1Esk6A1VdtF5e3LlWxRlpfkx8\nOs2Xxq29rOo6dER8Ls2viiekeVX/J9+Wd/U7KOl2eFkclY79qTStu9FUsvHVMS647PX5QX7mn/L8\npgfk+e15fM1R/fe6o+KydOyu0YtreA09Oz41aPwkDOkudG+yusu65U1bdc1F5L+3DLUyvj2R49zX\nFhfduKr75dCOWVnnrc3F2Op9YWhnxIOTHmtVB6zq2HvFxjQf0nWumrd6zusuhTnf7AEAAAAYEYs9\nAAAAACNisQcAAABgRCz2AAAAAIyIxR4AAACAEdGNa555Xbyzl1W76Fc7xp8cb07z0+PMWc9ZzXtB\nvCYd+9U4LM0rr4kL0nxRskt5tXP5pHpIVc9X9viPjksnNGsu29W96oBU7S5f7SS/NDbM/cSIiGG1\nEjGZGh065zRrNKvPiOnW6JD6jJhcjR4c3+hl64vOSNXjzzr9cP/10Xhemv9OnJ3mTyy60eVdtyJW\nxdN62ep/l5/LlxY8Jv9BXFPk973qvagypGPQq+OP0vyMWJXml8Qxab4i1vayBxfdVd4dv5HmVZe+\nFXFzmt8Sy9M867xVdYCpDO3SMmR811r+gyIuFeOvjJ/tZdXrtqsYWkPXxsFpXnUvmqahc+6eXOtZ\nh7ppq7orTcqSosNS5o6d8LpV1saKNF8Sm9O8+h2l6jCVdXWruhFXdVHNWT3n2e9X1ZyVrItyRP25\nsPqdLjvHauzQ9wXf7AEAAAAYEYs9AAAAACNisQcAAABgRCz2AAAAAIyIDZrnmWyDwiGbFt7b+Gqj\nr2p8lg/d/K+y24A5s027Jql6/Atjy31+LtnmYtVGXNXmd0NzZm9IrczlOFmNDp1zmjVan8v06mJI\nfU7yXLbGolmPrWpr2vcL7lvXx8pB46sNGqPY6DvbjHn1/8qP8PK4ZdC5jE31GWJzsaFlJavdLUXt\n1/fcvP6H3EPu7TjzxSlPO23Q+HP+9qxBxzkmPj34nPjXhn4Wya65SRzj3lTHz2p6aA2NTdUUY2eY\n1GfL6jjZ56VNsTgdW/1eNImmANX1XH3m3BILi/H5a1cfvz++ei+qNm6u+GYPAAAAwIhY7AEAAAAY\nEYs9AAAAACNisQcAAABgRCz2AAAAAIyIblzzzBcvfFI/vDIfe9RFl6X5lS9/app//meelubP3Ouz\n+QTJvL/4tPwYl7zgmfkxCl955y/mP7gmyfYuDvJ7g6YsVc9X3N6PPtuKx/ln75rIuSyJTb2s2l1+\naNe1rNMXw6T1GTHVGh1SnxFTrtGsPiOmWqND6jNicjV6SyzvZVVniMoB8Z1B45nfboj90/y6OCjN\nT4h3pPlD7lia5l9a8JheVnXduqj9Q5qv7tJ4p3hJy7suPXpFPv6Em8+Z9bGPjY+k+Wfi6DR/fjF+\nVZzRy5bGhlmfR0TE4uR9OyLiY/HcND8p3pLmS2JzLzv56vwaisNeP7uTm6ChXZf+5klJe7l7Oc61\ncXAv21p0utlVDK2hI27+yzQ/O05N89ti9152fpwwu5O7U9Uxq+5SmX92PTyu6mU743Prae2JaX7q\nhO6tq994bv6Dj/Wjs248cTKTTsCiohtVZej9IuvGVnWd2ru4R1e/L1W/F2X3l+pxbowHp3nVGbLq\nGDbknlZ3ehx2X/TNHgAAAIARsdgDAAAAMCIWewAAAABGxGIPAAAAwIhsd7GntXZha21da+0bd8vO\nbK39TWvt6621v2it/cR0TxMAAACA2Whdd+/bi7fWfiF+1Pfkg13XPfrObPeu626/88+vjYiDuq47\nvvj73fbmAAAAAGD2WmvRdV3Lfrbdb/Z0XXdlRPzgHtndm94+MCJ+uENnCAAAAMBE7DbXv9haOzsi\nXhIRGyPiyImdEQAAAABzNufFnq7rTo2IU1trJ0XEayNidTV29ep/+dHMzEzMzMzMdVoAAACAXc6a\nNWtizZo1sxq73T17IiJaa4+MiEt+vGfPPX62IiIu7bru4OLv2rMHAAAAYIJ2aM+eHx/jzv/9+IAH\n3O1nvxIR35r76QEAAAAwKdv9Z1yttYsjYiYilrbWbo6IVRHxy621lRGxLSL+PiJeM82TBAAAAGB2\nZvXPuHZoAv+MCwAAAGCiJvHPuAAAAAC4H7DYAwAAADAiFnsAAAAARsRiDwAAAMCIWOwBAAAAGBGL\nPQAAAAAjYrEHAAAAYEQs9gAAAACMiMUeAAAAgBGx2AMAAAAwIhZ7AAAAAEbEYg8AAADAiFjsAQAA\nABgRiz0AAAAAI2KxBwAAAGBELPYAAAAAjIjFHgAAAIARsdgDAAAAMCIWewAAAABGxGIPAAAAwIhY\n7AEAAAAYEYs9AAAAACNisQcAAABgRCz2AAAAAIyIxR4AAACAEbHYAwAAADAiFnsAAAAARmS3nX0C\n/GvtwiT8i3zsWR89Mc1Pe955ad4d3PI5H9blEyTzds/Kj3Hci9+R5u+J1+Vzvi2fMr6UZHvnQ7sL\nimMM1J5X/GBjMuet+eOPrxfP4UBvjhN62YZYmo5dENvSfFssGJSfHyfP8uxI6zNiqjU6pD4jplyj\nWX1GTLVGh9RnxORq9MQ4u5dtiiWDjrEi1qb5yXH+oOMwP7w23prmn42npPmH48VpfmkcneYbY69Z\nn8tL2mlp/uhuMu9Fk7C65bW46iH5+E9veFov+3wckY49NK5O84vjRWl+epyZ5q+Ld/ayPeK2dOyy\nWJ/mK+P6NP9YPDfNfy/emOZXxJG97JyPnpWOjeflr/MpcXqan1M8/mp8pvoMMSlbY2Evqz7nnBen\nTvVc5ouhNfTsDRen+emRX0e3xR697Pz4rXTs1liU5tV1saUYvyDuSPPD46o0z5wV58x67FCtvT/N\nu+5lkzn+a4sf/Jd+tPofTkqHropzJ3IuQ2SfiSLqGt0Ui9N8r+LD26LY2suq98TlcUuar4t90nxh\nbEnzzclnuur+X33+q67/ZbEuzas6ygz5THBvfLMHAAAAYEQs9gAAAACMiMUeAAAAgBGx2AMAAAAw\nIhZ7AAAAAEZEN6755qYke3o+9LTj844+1fi4ssj/uciz4zwhH3pC2V0m7/QTM8XwbOPxZIf6iaqe\nr9v70YY35EPzflnDTbPbxR1T7qSxS7ipyKdZo0PqM2K6NVo1BphmjQ6oz4jJ1WjWYaKqz6obRdV1\nhPunbcVHpqpLR9XVo7pehnj0ih0+xNRVHYPO+H6er/70X/ayZ32zn0VEnHBK3o2n6uizJDaleda9\nparzg+Pa4hjL0rzqRnNtHJzm+8WN/fDmdOhUTbvr1hDz6Vx2hqE19N1YnuY3xb5pnnXj2lC01xza\n6fWQuGbWc0bknZGGdsCcjF+c7uH3LfKfme6001K9/rsNfJ/L7tFVN6qhv88MPZfM0Ot/moZ+hvDN\nHgAAAIARsdgDAAAAMCIWewAAAABGZLuLPa21C1tr61pr30h+9obW2g9ba8W/KgUAAADgvjSbb/Zc\nFBFH3TNsrT08Ip4aEX8/6ZMCAAAAYG62242r67orW2uPTH50fkS8MSI+PfGz2pU9vx8956c/nA79\n+KvytjvP6b6QH/unijmLDjvZvBfES9Oxx1/zgfwgh+TxSw+9IM1vP7S/S//H935xfpAJec4r8+c3\n2+196aTabhW2xMJetjC2pmOHdvrZ1btaTERSnxFTrtEB9Rkx3RrN6jNiujU6pD4jJlejC2NLL6s6\n+lQ1WnVG4f5pr9iY5vsWbfqqDjhDu7plTrg570ZV9dzbGT694WlpnnXdiohY/ax+9qri2FeccmSa\nvyV+O82vi4PS/Ij4XDFDX9Vd6EPxa2m+Itam+Zfj8Wmevf6/9pr8/veANJ2Mg+K6NL8+Vqb58rgl\nzddG3jKu7l644x1zxmZoDX23uEY/EsfOes7q9Ryq6oxXdVjKarR6z52mc7rfL34ymbvr6jeclOYL\n3tC//jdMrNfvjqs+51S2Jr/P/ChflOZZ/S8uXv+sc1t1jIi6q1s2vvpsWb3/V9dz1QFsQ9GNbsjj\nH2pOe/a01o6JiLVd1+V9KAEAAADYKbb7zZ57aq0tjohT4kf/hOuueGJnBAAAAMCcDV7siYj9I2Lf\niPib1lqLiIdHxFdba4/rum599hdWr159159nZmZiZmZmDtMCAAAA7JpuWvP38fdrbp7V2Nku9rQ7\n/xdd130zIn7irh+0dmNE/GzXdT+o/vLdF3sAAAAAGGbfmUfGvjP/sqXy58+4shy73cWe1trFETET\nEUtbazdHxKqu6y6625Au/DOuiXnAw7/fyx4XX07HfrxohPa4+F5+8EcUcz6oP2c177Xx6Pwg38rj\navPXR0e+3VO2MdbH953uBs3V85t60PTOo1Jv5jlsg2Z2XFafEdOt0SH1GTHdGq0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AEAAACYEJM9AAAAABMijWvJZKt9VyuaV/bE/rR+oEiSqFYYz45brca+qfMcd8ThuY+5aNX1OhZb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\nQv5/6Wh4EYKcw8YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112e739d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the correlation matrix\n",
    "size=20\n",
    "fig, ax = plt.subplots(figsize=(size, size))\n",
    "ax.matshow(corr)\n",
    "col_n = range(len(corr.columns)/8)\n",
    "col_i = map(lambda x: x*8, col_n)\n",
    "plt.xticks(col_i, col_n);\n",
    "plt.yticks(col_i, col_n);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6t1LPG8gDUd/sM/575A1+ZzwiDicPJpxBnj315JTSVUPUPUibTyevtzPayp4M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\nIEmSJEmSpCn3/wEsAoacaVjZmAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1131c3110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Std Deviation of columns in the correlation matrix\n",
    "# CRC bits should have low deviation; i.e. their correlation\n",
    "# across all the other bits should be uniform\n",
    "corr.std().plot('bar',figsize=(20,2)); None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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k/XDYPE/plGePbfcK8tmvD4xZnnW/Fm2rO88e2+468gf7i0awPpuUZ8d1CryT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1133ba590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Avg value of bits by column. Shows bias.\n",
    "# CRC bits should have small bias (small bias = closer to 0.5)\n",
    "bits_df.mean().plot('bar',figsize=(20,2)); None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11602bd10>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x112428950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Byte-wise correlation matrix\n",
    "\n",
    "def hex_unpackbytes(hex_str):\n",
    "    return np.frombuffer(hex_str.decode('hex'), dtype=np.uint8)\n",
    "\n",
    "# Generate a correlation matrix of the message bits\n",
    "bytes = map(hex_unpackbytes, list(fixed_length[\"raw_packet\"].values))\n",
    "bytes_df = pd.DataFrame(bytes)\n",
    "corr = bytes_df.corr()\n",
    "\n",
    "# Plot the correlation matrix\n",
    "size=20\n",
    "fig, ax = plt.subplots(figsize=(size, size))\n",
    "ax.matshow(corr)\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
